Companies are redefining their businesses by building models and learning from data. Whether it is using data science to predict their best sales and marketing targets, automating digital customer interactions using bots, or reducing waste in logistics and manufacturing - Artificial Intelligence will improve your business once deployed.

Serving up good predictions at the right time to drive the appropriate action is hard. It requires setting up data streams, transforming data, building models and delivering predictions. Most approach this by building single models and realizing along the way that data science is only the beginning. The engineering and infrastructure required to maintain a single model and ship the predictions present even more challenges.

Trying to replicate this success for more models or customers is even more difficult. Most approach it by building a handful of additional models, painstakingly addressing challenges by taking one-off approaches to handling increasing volumes of data, differences in data, changes in process, etc. Scaling to 1000s of customers becomes impossible.

At Salesforce we built the Einstein Platform to enable the automation and scaling of Artificial Intelligence to 1000s of customers, each with multiple models. The data ingestion, automated machine learning, instrumentation and intelligent monitoring and alerting make it possible to serve the varied needs of many different businesses. In this talk we will cover the nuts and bolts of the system, and share how we learned to solve for scale and variability with a fully operational Machine Learning platform.